Manager AI Engineer

You will be working as a Lead Engineer, Machine Learning Engineering for the Operational Intelligence Program within Mastercard's Business & Market Insights (B&MI) group. Your role will involve leading the ML engineering team to execute the AI/ML strategy for the program, enabling business growth, enhancing customer experience, and ensuring the delivery of secure, scalable, and high-performing software solutions. You will also focus on engineering best practices, next-gen innovation, stakeholder management, and fostering a culture of continuous learning and technical excellence within the team. Key Responsibilities: - Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents. - Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence. - Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning. - Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems. - Implement advanced Python-based backend services for inference orchestration, async job handling, and distributed data workflows supporting high-throughput AI operations. - Establish end-to-end LLMOps and MLOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining frameworks. - Apply traditional AI/ML and statistical modeling techniques (regression, clustering, forecasting, ensemble methods) alongside deep learning models for hybrid interpretability and explainability. - Engineer state and memory management subsystems that preserve context, track embeddings, and enable agents to reason temporally across multiple modalities and interactions. - Implement Responsible AI practicesbias detection, explainability dashboards, data ethics checks, and performance governance ensuring fairness and transparency of deployed models. - Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration to drive enterprise-scale intelligence and automation. Qualifications Required: - Masters/bachelors degree in computer science or engineering. - Considerable work experience with a proven track record of successfully leading and managing complex projects/products. - Expert-level hands-on experience designing, building, and deploying both conventional AI/ML solutions and LLM/Agentic solutions. - Strong analytical and problem-solving abilities with quick adaptation to new technologies, methodologies, and systems. - Strong applied knowledge and hands-on experience in advanced statistical techniques, predictive modeling, machine learning algorithms, GenAI, and deep learning frameworks. - Experience with AI and machine learning platforms such as TensorFlow, PyTorch, or similar. - Strong programming skills in languages such as Python/SQL is a must. - Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus. (Note: Additional details about the company were not provided in the job description.) You will be working as a Lead Engineer, Machine Learning Engineering for the Operational Intelligence Program within Mastercard's Business & Market Insights (B&MI) group. Your role will involve leading the ML engineering team to execute the AI/ML strategy for the program, enabling business growth, enhancing customer experience, and ensuring the delivery of secure, scalable, and high-performing software solutions. You will also focus on engineering best practices, next-gen innovation, stakeholder management, and fostering a culture of continuous learning and technical excellence within the team. Key Responsibilities: - Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents. - Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence. - Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning. - Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems. - Implement advanced Python-based backend services for inference